Detection of rice pests based on RepMob-Yolo

Abstract Aiming at the disadvantages of high labor intensity, poor real-time performance, and low efficiency of traditional methods for identifying rice pests, a rice pest identification model based on RepMob-Yolo is proposed. MobileNetV3 is used as the backbone network, and the RepMob structure is used to replace the conventional downsampling structure in MobileNetV3, the GELU is used to replace the ReLU and the last four convolutional layers of MobileNetV3, the global average pooling layer and the fully connected layer are pruned to realize the lightweight design of the model. The DANet module is embedded to realize the extraction of effective information by the model, extract the semantic information of higher key features, and improve the recognition accuracy. The FPN structure is combined with the RepMob structure for feature extraction and prediction, which improves the operation speed of the model. The experimental results show that RepMob-Yolo achieves 97.68% accuracy, 96.00% recall, 96.82% prediction rate, and 146.37 f/s frame rate in the identification of public pest datasets, which provides technical support for accurate and real-time identification of rice pests.